Eddy Covariance Gross Primary Productivity Research Articles

The Difference between the Responses of Gross Primary Production and Sun-Induced Chlorophyll Fluorescence to the Environment Based on Tower-Based and TROPOMI SIF Data

The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy a stable GPP–SIF relationship. Therefore, exploring the difference between responses of GPP and SIF to the environment is essential to correctly understand the GPP–SIF relationship. As the common driver of GPP and SIF, the incident radiation could cause GPP and SIF to have similar responses to the environment, which may obscure the discrepancies in the responses of GPP and SIF to the other environmental variables, and further result in the ambiguity of the GPP–SIF relationship and uncertainties in the application of SIF. Therefore, we tried to exclude the dominant role of radiation in the responses of GPP and SIF to the environment based on the binning method, in which continuous tower-based SIF, satellite SIF, and eddy covariance GPP data from two growing seasons were used to investigate the differences in the responses of GPP and SIF to radiation, air temperature (Ta), and evaporation fraction (EF). We found that the following: (1) At both the site and satellite scales, there were divergences in the light response speeds between GPP and SIF which were affected by Ta and EF. (2) SIF and its light response curves were insensitive to EF and Ta compared to GPP, and the consistency in GPP and SIF light responses was gradually improved with the improvement of Ta and EF. (3) The dynamic slope values of the GPP–SIF relationship were mostly caused by the different sensitivities of GPP and SIF to EF and Ta. Our results highlighted that GPP and SIF were not highly consistent, having differences in environmental responses that further confused the GPP–SIF relationship, leading to complex SIF application.

A water stress factor based on normalized difference water index substantially improved the accuracy of light use efficiency model for arid and semi-arid grasslands

High-accuracy simulation of gross primary productivity (GPP) is crucial for the monitoring and evaluation of the ecosystem services and the adaptive management of grassland. The light use efficiency (LUE) model is one of the most widely-used methods to simulate GPP, given its simple structure and low input requirements. Current LUE models are less applicable to grasslands than other vegetation types and have lower overall estimation accuracy in arid and semi-arid regions. A grassland-specific light use efficiency model (GRASS-LUE), which optimizes three important parameters (the fraction of absorbed photosynthetically active radiation FPAR, optimum temperature Topt and water stress factor f(W)), has been developed to improve the accuracy of GPP simulation for grasslands along aridity gradients. GPP simulated by the GRASS-LUE agreed well with the eddy covariance (EC) GPP estimates for grasslands along the aridity gradient at 8-day (coefficient of determination (R2) = 0.85, Bias = −0.67 gC m−2 day−1), monthly (R2 = 0.88, Bias = −22.33 gC m−2 month−1) and annual time scales (R2 = 0.95, Bias = −118.91 gC m−2 year−1). Compared with five state-of-the-art GPP products (PML, MOD17, rEC-LUE, VPM and BESS), GRASS-LUE had the best and most stable performance in reproducing EC GPP, especially for semi-arid grassland, with the highest global performance indicator (GPI) value. Sensitivity tests further revealed that: 1) modifying f(W) to be based on the Normalized Difference Water Index (NDWI) substantially improved the model accuracy for arid and semi-arid grasslands and 2) using the minimum of temperature and water stress factors (i.e., min⁡(f(W),f(T))) to represent environmental stress in GRASS-LUE was better than that from the multiplication of temperature and water stress factors (i.e., f(W)×f(T)).

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.

Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation

Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is applied in GPP estimation by light use efficiency (LUE) models. However, an obvious time scale mismatch exists between instantaneous FAPAR and daily accumulated GPP. In the study, we explored the potential of temporal upscaling instantaneous FAPAR in improving GPP simulation. The absorbed photosynthetically active radiation (APAR) derived from instantaneous and upscaled FAPARs were first compared. GPPs were estimated from instantaneous and upscaled FAPAR by MOD17 LUE model using default and optimized parameters. The optimized GPP was finally compared with three GPP products: GLASS, MOD17A2H and MYD17A2H GPP. The APARs and GPPs were evaluated with the Eddy Covariance (EC) GPP at 78 forest sites from the Fluxnet community. Results showed that when compared with EC GPP, the upscaled FAPAR-derived APAR held a higher R2 (0.26–0.73) than that from instantaneous FAPARs. The upscaled FAPAR-based GPPs by MOD17 LUE model with default or optimized parameters had a larger R2 and smaller RMSE reducing uncertainties by 6.82–7.8% (maximum value: 27.9–49%). The optimized upscaled FAPAR-derived GPP was far superior to the GPP products with larger R2 (0.69–0.85) and smaller RMSE (12.8–14.6 g C/m2/8d). It is concluded that temporal upscaling of MODIS instantaneous FAPAR can well improve the estimation of forest GPP and the parameters of MOD17 LUE model should be further optimized. Our study is an effort toward reducing the uncertainties from FAPAR in the GPP estimation for better assessing the achievement of carbon neutrality and Sustainable Development Goals.

Comparison of big-leaf and two-leaf light use efficiency models for GPP simulation after considering a radiation scalar

Light use efficiency (LUE) models, mainly including the big-leaf (BL) and two-leaf (TL) categories, are efficient approaches to simulate gross primary productivity (GPP). Recently, a TL LUE model considering a radiation scalar (RTL-LUE) was developed, which improved the GPP simulation and unified the same model structure with the BL models using only one maximum LUE. However, whether the radiation scalar is suitable for BL models is still unknown yet, and the core parameters of maximum LUE in the BL and TL models have not been fairly compared before. In this study, we step forward to modifying the MOD17 model using the radiation scalar (RMOD17), and compare the GPP simulations and maximum LUEs in BL (MOD17, RMOD17) and TL (RTL-LUE) models at global 169 FLUXNET eddy covariance (EC) sites. Results indicate that the GPP estimation from RMOD17 (R2=0.72) matches better with EC GPP than those from the original MOD17 (R2=0.65), because the radiation scalar effectively corrects the underestimations or overestimations in low or high photosynthetically active radiation (PAR) ranges caused by the radiation-independent LUE in MOD17. The RTL-LUE can further improve the accuracy (R2=0.74) by alleviating the GPP underestimation of BL RMOD17 in high productivity ranges, which is mainly caused by the insufficient consideration of the shaded leaves contributions. The maximum LUE from RMOD17 and RTL-LUE both show more reasonable values and lower diurnal variations than MOD17, and further analysis proves the radiation scalar is the main reason for this. Besides, RTL-LUE presents a lower model parameter sensitivity compared to RMOD17, indicating that TL is a more robust strategy than BL to simulate GPP. This study highlights the importance of considering a radiation scalar and two-leaf strategy in GPP simulation in LUE models, to better describe the multi-order impacts of radiation on vegetation photosynthesis.

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj < 0.0001) and EC GPP (79.6 days, padj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam GCC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m−2 yr−1. Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland

Abstract. Arid and semiarid ecosystems contain relatively high species diversity and are subject to intense use, in particular extensive cattle grazing, which has favored the expansion and encroachment of perennial thorny shrubs into the grasslands, thus decreasing the value of the rangeland. However, these environments have been shown to positively impact global carbon dynamics. Machine learning and remote sensing have enhanced our knowledge about carbon dynamics, but they need to be further developed and adapted to particular analysis. We measured the net ecosystem exchange (NEE) of C with the eddy covariance (EC) method and estimated gross primary production (GPP) in a thorny scrub at Bernal in Mexico. We tested the agreement between EC estimates and remotely sensed GPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), and also with two alternative modeling methods: ordinary-least-squares (OLS) regression and ensembles of machine learning algorithms (EMLs). The variables used as predictors were MODIS spectral bands, vegetation indices and products, and gridded environmental variables. The Bernal site was a carbon sink even though it was overgrazed, the average NEE during 15 months of 2017 and 2018 was −0.78 gCm-2d-1, and the flux was negative or neutral during the measured months. The probability of agreement (θs) represented the agreement between observed and estimated values of GPP across the range of measurement. According to the mean value of θs, agreement was higher for the EML (0.6) followed by OLS (0.5) and then MODIS (0.24). This graphic metric was more informative than r2 (0.98, 0.67, 0.58, respectively) to evaluate the model performance. This was particularly true for MODIS because the maximum θs of 4.3 was for measurements of 0.8 gCm-2d-1 and then decreased steadily below 1 θs for measurements above 6.5 gCm-2d-1 for this scrub vegetation. In the case of EML and OLS, the θs was stable across the range of measurement. We used an EML for the Ameriflux site US-SRM, which is similar in vegetation and climate, to predict GPP at Bernal, but θs was low (0.16), indicating the local specificity of this model. Although cacti were an important component of the vegetation, the nighttime flux was characterized by positive NEE, suggesting that the photosynthetic dark-cycle flux of cacti was lower than ecosystem respiration. The discrepancy between MODIS and EC GPP estimates stresses the need to understand the limitations of both methods.

First Investigation of the Relationship Between Solar-Induced Chlorophyll Fluorescence Observed by TanSat and Gross Primary Productivity

Reliable estimation of gross primary production (GPP) of terrestrial ecosystems is crucial in the global carbon cycle and ecosystem functioning studies. Solar-induced chlorophyll fluorescence (SIF) has emerged as an unprecedented proxy for terrestrial GPP estimation. TanSat SIF with a 2-km fine spatial resolution provides an excellent opportunity to investigate the relationships of satellite SIF and eddy covariance (EC) GPP at ecosystem or site scale, while has not been investigated. In this study, we investigated the relationship between TanSat SIF and EC GPP based on flux tower sites encompassing different biomes over the globe. TanSat SIF exhibited strong relationships with EC GPP at Fe and KI bands for instantaneous and daily timescales. The correlations between EC GPP and TanSat SIF (R2 = 0.61) were much better than moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)- corrected vegetation indices (VIs) (R2 = 0.480.58). Further comparisons between SIF and GPP with absorbed photosynthetically active radiation (APAR) and two environmental factors (fTmin and fVPD) showed that SIF responded to APAR and was similarly affected by environmental factors as GPP. GPP derived from SIF (R2 = 0.64) also performed slightly better than enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) and even light use efficiency MODIS GPP algorithm (R2 = 0.580.60). Therefore, our results demonstrated the feasibility of SIF on estimating GPP at sites or ecosystem levels; in addition, incorporating TanSat SIF with other satellite SIF products will open a new era for carbon cycling and ecosystem functioning studies.

Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau

Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.

Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas

The temperature and greenness model (TG) demonstrates that the combination of enhanced vegetation index and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is feasible in obtaining gross primary productivity (GPP) at the landscape, regional, and global scales. However, the input LST data of TG is always available at a coarse resolution (~ 1 km), averaging a relatively large portion of the topographic characteristics. Hence, GPP simulated using the coarse spatial resolution LST data would suffer from unavoidable bias over mountainous areas. Considering the above limitation, this work proposed a mountainous temperature and greenness model (MTG) through integrating an elevation-corrected factor and a radiation-corrected factor with the current TG model. The proposed MTG model was validated at sixteen eddy covariance (EC) sites with apparent topography in the carbon footprint areas. Results showed that MTG-simulated GPP presented a better agreement with EC GPP than TG-simulated GPP, characterized by an increase of 0.06 in R2 and a decrease of 5.43 gC m − 2 8d−1 in root mean square error, suggesting that the MTG model had a better feasibility of capturing the GPP variations over mountainous areas than the TG model. The standard deviation of MTG-simulated GPP at the sixteen study sites varied between 3.29 and 22.79 gC m − 2 8d−1, highlighting the importance of considering topography within coarse pixels when obtaining GPP estimates over mountainous areas. Furthermore, results also indicated that the MTG-simulated GPP showed obvious responses to topography, suggesting that the MTG model could adequately characterize the topographic effects on plant photosynthesis. More specifically, MTG-simulated GPP increased when slope increased in the sunlit terrains, while it was found to have a lower value when slope increased in the shaded terrains. Our study suggests that incorporating topography information into current GPP models is a practical approach to improve GPP estimates over mountainous areas.

An Adjusted Two‐Leaf Light Use Efficiency Model for Improving GPP Simulations Over Mountainous Areas

AbstractRecently, light use efficiency (LUE) models have been widely used for calculating gross primary productivity (GPP). Unfortunately, these models are only suitable for flat areas without considering the topographic effects on plant photosynthesis. In this study, considering the topographic controls on direct radiation, diffuse radiation, and sunlit canopy area, an adjusted model called mountainous two‐leaf (MTL)‐LUE is developed from the original two‐leaf LUE model (TL‐LUE) to improve the GPP simulations in mountainous areas. The MTL‐LUE model was validated at an eddy covariance (EC) site with apparent topography in the carbon footprint area. Results showed that the daily GPP simulations from MTL‐LUE (root mean square error = 1.29 gC m−2 d−1) matched better with EC GPP than those from the TL‐LUE model (root mean square error = 1.84 gC m−2 d−1), confirming the improvement of the MTL‐LUE model in mountainous areas. Moreover, results also indicated that the MTL‐LUE‐simulated GPP showed obvious spatial variations in the study area, suggesting that the MTL‐LUE model could characterize the effects of topography on plant photosynthesis. More specifically, canopy GPP was found to decrease when slope increased in shaded terrains and has no obvious variations in different slope ranges when exposed to the sun. Furthermore, results also indicated that a moderate increase in canopy GPP would be expected when the sky clearness decreased, which depended on the gap between the GPP increase of shaded leaves and the GPP decrease of sunlit leaves. This study highlights the potential of the MTL‐LUE model in improving GPP simulations in mountainous areas.

Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere

The accurate quantification of gross primary productivity (GPP) has been a major challenge in global climate change research. Satellite data-driven models have been universally used as scientific tools for investigating the carbon cycle, including vegetation index (VI)-based models, light use efficiency (LUE) models, and process-based models. However, inconsistencies and uncertainties have been found in the GPP estimations from various models. The understanding of model behaviors under different climatic conditions remains unclear. In this study, three typical satellite data-driven models, namely, Moderate Resolution Imaging Spectroradiometer (MODIS) GPP (MOD17) model, Temperature and Greenness (TG) model and Boreal Ecosystem Productivity Simulator (BEPS), respectively, were compared to better understand discrepancies and uncertainties in GPP estimations at 119 northern eddy covariance (EC) sites. Due to the variations in climatic drivers of GPP, temperature, precipitation and incoming solar radiation were selected to describe climatic conditions. The results showed that BEPS and MOD17 exhibited similar performance in simulating GPP, with root-mean-square error (RMSE) values of 2.50 g C m−2 d−1 and 2.53 g C m−2 d−1, respectively, and performed slightly better than TG (RMSE = 2.98 g C m−2 d−1). Comparison between simulated GPP and EC GPP also revealed that model performance varied substantially among different vegetation types. The three models performed better for deciduous broadleaf forest, evergreen needleleaf forest, and mixed forest, in comparison to the results from evergreen broadleaf forest and crop. Specifically, all three models showed poor performance under the conditions of high temperature and low precipitation, revealing the models’ inability to characterize the impact of water stress on photosynthesis when drought occurs. Furthermore, our results indicated that GPP estimations from satellite data-driven models were also sensitive to remotely sensed data, suggesting that the high accuracy of remotely sensed data in describing vegetation canopy is important for carbon modeling. This study highlights the importance of understanding model behaviors in different vegetation types and climatic conditions, so that the model performances may be improved in future carbon cycle studies.

Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models

Ecosystem models have been widely used for obtaining gross primary productivity (GPP) estimations at multiple scales. Leaf area index (LAI) is a critical variable in these models for describing the vegetation canopy structure and predicting vegetation-atmosphere interactions. However, the uncertainties in LAI datasets and the effects of their representation on simulated GPP remain unclear, especially over complex terrain. Here, five most popular datasets, namely the Long-term Global Mapping (GLOBMAP) LAI, Global LAnd Surface Satellite (GLASS) LAI, Geoland2 version 1 (GEOV1) LAI, Global Inventory Monitoring and Modeling System (GIMMS) LAI, and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, were selected to examine the influences of LAI representation on GPP estimations at 95 eddy covariance (EC) sites. The GPP estimations from the Boreal Ecosystem Productivity Simulator (BEPS) model and the Eddy Covariance Light Use Efficiency (EC-LUE) model were evaluated against EC GPP to assess the performances of LAI datasets. Results showed that MODIS LAI had stronger linear correlations with GLASS and GEOV1 than GIMMS and GLOMAP at the study sites. The GPP estimations from GLASS LAI had a better agreement with EC GPP than those from other four LAI datasets at forest sites, while the GPP estimations from GEOVI LAI matched best with EC GPP at grass sites. Additionally, the GPP estimations from GLASS and GEOVI LAI presented better performances than the other three LAI datasets at crop sites. Besides, the results also showed that complex terrain had larger discrepancies of LAI and GPP estimations, and flat terrain presented better performances of LAI datasets in GPP estimations. Moreover, the simulated GPP from BEPS was more sensitive to LAI than those from EC - LUE, suggesting that LAI datasets can also lead to different uncertainties in GPP estimations from different model structures. Our study highlights that the satellite-derived LAI datasets can cause uncertainties in GPP estimations through ecosystem models.

Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites

Abstract. This study evaluates the performances of the new version (v.5.1) of 3D-CMCC Forest Ecosystem Model (FEM) in simulating gross primary productivity (GPP), against eddy covariance GPP data for 10 FLUXNET forest sites across Europe. A new carbon allocation module, coupled with new both phenological and autotrophic respiration schemes, was implemented in this new daily version. Model ability in reproducing timing and magnitude of daily and monthly GPP fluctuations is validated at intra-annual and inter-annual scale, including extreme anomalous seasons. With the purpose to test the 3D-CMCC FEM applicability over Europe without a site-related calibration, the model has been deliberately parametrized with a single set of species-specific parametrizations for each forest ecosystem. The model consistently reproduces both in timing and in magnitude daily and monthly GPP variability across all sites, with the exception of the two Mediterranean sites. We find that 3D-CMCC FEM tends to better simulate the timing of inter-annual anomalies than their magnitude within measurements' uncertainty. In six of eight sites where data are available, the model well reproduces the 2003 summer drought event. Finally, for three sites we evaluate whether a more accurate representation of forest structural characteristics (i.e. cohorts, forest layers) and species composition can improve model results. In two of the three sites results reveal that model slightly increases its performances although, statistically speaking, not in a relevant way.

The Carbon Cycle of a Maritime Ancient Temperate Broadleaved Woodland at Seasonal and Annual Scales

This study compares different approaches to quantifying the carbon cycle in a temperate deciduous forest at Wytham Woods in England, which is unusual in its maritime climate and mixed age structure, reflecting low levels of past management. We tested whether eddy covariance and biometric measurements gave consistent estimates of woodland productivity and ecosystem respiration at monthly and annual timescales. Biometric methods estimated gross primary productivity (GPP) as 22.0 ± 1.6 Mg C ha−1 y−1, close to the eddy covariance GPP value of 21.1 Mg C ha−1 y−1. Annual ecosystem respiration (R ECO) was similar, at 20.3 ± 1.5 Mg C ha−1 y−1 for biometric and 19.8 Mg C ha−1 y−1for eddy covariance. The seasonal cycle of monthly biometric and eddy covariance R ECO estimates also closely matched. Net primary productivity (NPP) was 7.0 ± 0.8 Mg C ha−1 y−1, 37% of which was allocated below ground. Leaf fluxes were the greatest component of NPP and R ECO. Ecosystem carbon-use efficiency (CUE = NPP/GPP) was 0.32 ± 0.04; low compared to many temperate broadleaved sites but close to values for old-growth sites. This may reflect the age of some trees, and/or the oceanic climate with relatively mild winters during which there can be substantial autotrophic maintenance respiration in winter but negligible growth. This study demonstrates that biometric measurements can provide robust estimates of site productivity and respiration and that eddy covariance and bottom-up measurements can be combined on seasonal and interannual timescales to enable a detailed understanding of the forest carbon cycle.

Evaluation of MODIS gross primary productivity for Africa using eddy covariance data

MOD17A2 provides operational gross primary production (GPP) data globally at 1km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (εmax). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate εmax and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred εmax calculated from tower data was higher than the εmax prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2.

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.

Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product

Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500m reflectance data and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R2=0.45–0.86, Coniferous: R2=0.49–0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1km daytime LST (land surface temperature) (R2=0.55–0.81, 0.57–0.77) and between GPP and EVI2, PPFD and daytime LST in multiple linear regressions (R2=0.73–0.89, 0.65–0.93). One year of data was collected from all coniferous sites to derive a general empirical model for GPP versus (1) the product of EVI2 and PPFD (R2=0.70), (2) the product of EVI2 and daytime LST (R2=0.62) and (3) EVI2, PPFD and daytime LST (R2=0.72). These three models were then validated at six sites for the remaining years by linearly relating eddy covariance GPP to modeled GPP, which resulted in fairly strong to strong relationships for most sites (R2=0.49–0.91, RMSE=0.63–1.22gCm−2day−1, R2=0.53–0.73, RMSE=0.90–1.43gCm−2day−1, R2=0.56–0.87, RMSE=0.79–1.11gCm−2day−1). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1km GPP product (R2=0.59–0.88, RMSE=0.80–1.16gCm−2day−1). The main conclusion is that the suggested empirical models driven by MODIS 500m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1km GPP product.

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem. Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific emax (a) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September). Important Findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.